EP3019080B1 - Procédé d'évaluation automatique d'un eeg de diagnostic d'absences, programme informatique et appareil d'évaluation correspondant - Google Patents

Procédé d'évaluation automatique d'un eeg de diagnostic d'absences, programme informatique et appareil d'évaluation correspondant Download PDF

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EP3019080B1
EP3019080B1 EP15702706.1A EP15702706A EP3019080B1 EP 3019080 B1 EP3019080 B1 EP 3019080B1 EP 15702706 A EP15702706 A EP 15702706A EP 3019080 B1 EP3019080 B1 EP 3019080B1
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eeg
scheme
computer
curves
stage
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EP3019080A1 (fr
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Arthur Schultz
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/369—Electroencephalography [EEG]
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • A61B5/721—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4094—Diagnosing or monitoring seizure diseases, e.g. epilepsy
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems

Definitions

  • the invention relates to a method for automatic evaluation of a Absence EEG according to claim 1.
  • the invention further relates to a computer program for carrying out such a method according to claim 11 and to an evaluation device for evaluating a Absence EEG according to claim 12.
  • the invention relates to the field of automatic evaluation of a Absence EEG, as already described with reference to WO 97/15013 A2 , of the WO 2010/034305 A1 or the WO 2010/034270 A1 is described.
  • EEG is the short form of the term electroencephalogram. This is done from EEG curves, ie from successively recorded values of EEG signals of a patient, by means of computational methods, eg. For example, based on statistical methods, a classification of the EEG made in which determines the current depth of the Absens-state of the patient and a current stage of the Absens-state is determined based on the classification and spent.
  • Absence state is understood to be any state of the patient in which the patient is not addressable or contactable according to age, this is the case when the patient is not awake.
  • Typical Absens states are z. B. at anesthesia, z.
  • anesthesia and sedation are conditions caused by administration of sleep-inducing drugs. If intensive care patients receive sleep-inducing medication, then one speaks i. A. of sedation. The term sedation is not limited to intensive care patients. If z. For example, in diagnostic procedures patients receive sleep-inducing drugs in low doses, then one speaks of sedation. For this purpose, for. B. intravenously administered sleep-inducing substances.
  • narcosis and sedation may use volatile anesthetics based on fluoran, such as sevoflurane. These can trigger seizure potentials with increasing dosage, which can be detected in the recorded EEG curves with appropriate evaluation and optionally masked out, as already explained in the aforementioned prior art.
  • absentee EEG or absentee state includes cases in which the brain function of a patient is changed from a normal state in the sense of attenuation.
  • special patterns such as epilepsy-typical activity, can occur in the EEG.
  • the recorded EEG curves are subject to certain changes as a result of the progressive development of a human being. Particularly significant changes are in the development of young people, d. H. from children to the transition to adulthood. Especially within the first year of life, the EEG curves develop very clearly.
  • the known method is to be further improved in order to allow a reliable determination of the current stage of Absens state, especially in very young patients within the first years of life.
  • This object is achieved according to claim 1 by a method for the automatic evaluation of a Absens EEG, recorded in the EEG curves of an evaluation and evaluated by a computer of the evaluation, which in the evaluation of the EEG curves by means of the computer using a Staging the Absence EEG at least the current stage of the Absens-state of a patient is determined, wherein in the staging stages of the depth of the Absens-state are distinguished, and wherein the current stage is output, wherein the computer a particular scheme of staging of several selectable schemes of staging, which differ by the number of distinguishable stages of the Absens condition, is selected and used for automatically performing staging to determine the current staging.
  • the invention has the advantage that a development or age-adapted staging of Absens EEG can be performed and appropriately customized information can be displayed to the user. These are determined with high reliability. In particular, a flexible adaptation to very young patients can take place. In newborns or very young children, as recent findings show, only a few Absens-EEG stages are distinguishable. Depending on the maturity of the brain, the number of distinguishable stages increases.
  • the evaluation can z. B. be designed as a compact device that is placed in the vicinity of a patient.
  • the evaluation device can also be designed as a multi-component device, the components also distributed, for. B. in different rooms of a building, can be arranged.
  • So z. B. the evaluation a data recording station for recording the EEG curves and a remote therefrom computer, z.
  • a central computer in an intensive care unit for an online evaluation of the EEG curves or a computer for offline evaluation of the EEG curves, have.
  • the invention is provided to check, after the start of the Absence state, whether the EEG curves have certain features by means of which it can be decided which scheme of the staging will be used from then on.
  • the selection of the particular scheme from several selectable schemes of staging can therefore be done once just after the beginning of the stalemate. It is also possible to continue to analyze the EEG curves during the Absence state for features that allow the scheme of the staging to be selected and, if desired, to switch from a once-selected scheme to another selected scheme in later operation.
  • the current state of the Absence state is output in this case, eg. B. by it is transmitted via an interface of the evaluation device to another device or is displayed visually on the evaluation device, z. B. on a display.
  • an additional information about the currently selected scheme of staging is output, z. B. by output to the said interface or visual representation on the evaluation. This allows the user of the evaluation device a quick and intuitive assessment of the output data.
  • Electroencephalography is a method of representing brain-generated electrical activity. In a conventional manner, the registration of the EEG with a multi-channel recorder on continuous paper. Increasingly, the recording is also made with the help of computers.
  • the composition of the waveforms in the electroencephalogram (EEG) depends on the functional state of the brain.
  • EEG images that occur in patients in the surgical and intensive care areas are diverse and can be influenced by a large number of endogenous and exogenous factors.
  • the normal wake EEG is z.
  • sleep EEG elements, effects of drugs and other exogenous chemicals, ventilatory and metabolic influences, temperature effects, sequelae of traumatic brain lesions, and inflammatory, vascular, degenerative, and neoplasm-induced EEG changes may be expected.
  • the following frequency ranges are assigned to the waves occurring in the EEG: Alpha (7.5 - 12.5 Hz), Beta (> 12.5 Hz), Theta (3.5 - 7.5 Hz) and Delta (0.5 - 3.5 Hz).
  • the Subdelta- ( ⁇ 0.5 Hz) and the gamma band (> 30 Hz) are demarcated.
  • the findings describe the waves in frequency ranges in terms of their amplitudes, frequency, regularity, temporal organization, local distribution and change in stimuli. EEG amplitudes are measured in ⁇ V. Higher frequency waves usually have smaller amplitudes, while slowing usually involves an increase in amplitude.
  • anesthetic or coma-EEG stages Kugler proposes an EEG classification, in which the awake state with A and EEG images with progressive attenuation of brain function with the letters B to F are designated.
  • the EEG curves are evaluated using the frequency and amplitude of the waves in certain frequency ranges as well as typical patterns.
  • stage A The wake EEG, stage A, is characterized by waves in the alpha frequency range in the majority of adults.
  • Stage B is characterized by fast frequency, low amplitude waves.
  • stages C and D theta and delta waves occur.
  • stage E high amplitude delta activity determines the plot.
  • stage F is characterized by a change from flat to isoelectric curves and groups of higher waves, the burst-suppression pattern, or by a continuous very flat activity.
  • the further processing of the results of the Fourier transformation comprises the extraction of so-called spectral parameters as well as further statistical calculations.
  • the parameters that can be derived from the spectrum include z. B. the total power as well as absolute and relative power in different frequency bands.
  • Other commonly used parameters are the median, the spectral edge frequency and the dominant frequency.
  • the median is the frequency at which the area of the spectrum is divided into two equal parts.
  • the Spectral Edge Frequency is usually defined as 95% quantile, i. H. 95% of the total power of the spectrum is below this frequency.
  • the dominant frequency is the frequency with the highest power.
  • FFT Fast Fourier Transform
  • AR autoregressive
  • a measurement at a given time is represented as a weighted sum of its historical values plus a random component.
  • the weights are the AR parameters.
  • p is the AR parameters and e t independent random components with mean 0 and constant variance for all time points t.
  • the letter p denotes the order of the process, ie the number of historical values that are taken into account.
  • the model parameters can be estimated using the Yule-Walker equation. For the determination of the order of the model and the check of the model quality the approach of Box and Jenkins is usually used. An overview of further estimation methods and model classes is given by Kay and Marple.
  • Hjorth A commonly used method for characterizing EEG measurements is the calculation of specific EEG parameters proposed by Hjorth and named after him. These are three parameters: activity, mobility and complexity.
  • the Hjorth parameters are calculated from the scatter of the EEG signal as well as its first and second derivatives.
  • the calculation of the Hjorth parameters can also be performed in the frequency domain, i. H. be made with the help of spectral analysis.
  • the activity corresponds to the total power of the signal and is therefore a measure of the amplitude size of the EEG measurement. Mobility can be interpreted as a measure of mean frequency and complexity as a measure of the variability of the signal.
  • Discriminant analytical classification methods are suitable for assigning objects to one of several defined groups based on a series of raised features.
  • the EEG sections form the objects to be classified which are characterized by spectral parameters and / or AR parameters and / or Hjorth parameters and / or chaos parameters.
  • suitable classification functions there are a number of methods in which parametric and nonparametric approaches can be distinguished. By means of a bar sample of objects for which the group affiliation is known, classification functions can be derived based on the considered feature values.
  • the linear discriminant analysis assumes the equality of the covariance matrices in the individual groups, the quadratic discriminant analysis allows the consideration of different covariance matrices of the groups.
  • the distance measure used is the Mahalanobis distance, which represents the weighted distance of an observation vector to the group mean values. An object is then assigned to the group in which a function of the Mahalanobis distance which is dependent on the selected method is the smallest.
  • nonparametric methods can be used to derive classification rules be used.
  • a vivid process is the k-wayneighbor method.
  • the distances of the feature vector to be classified to all the other feature vectors of the available random sample are formed, ordered by size, and the observation vectors with the k smallest distances are determined, wherein the number k of the values taken into account must be predetermined. Then it is determined to which groups these k values belong and their share of the total number of measurements in the individual groups. The assignment then takes place to the group in which this proportion is the largest.
  • This nonparametric method requires an increased amount of computation compared to parametric methods, since the classification of an object on the entire original data set must be used, while in parametric methods, the feature values of an object in classification functions are used.
  • the associated error rate can be used, where error rate is understood to mean the proportion of incorrect classifications.
  • One way to estimate the error rate is to reclassify the data.
  • the error rate thus determined provides a too positive estimate of the true error rate.
  • a more realistic estimate of the error rate is given when checking the classifications on an independent data set. This can be done by splitting the given data set into a training data record for deriving the classification rule and a test data record for validating the classification.
  • An extreme form of splitting the data consists in the so-called crossvalidation or the leave-one-out procedure. In each case one observation is taken out of the data record and the classification is made on the basis of the discriminant function calculated from the remaining data.
  • suitable parameters can be determined by means of suitable stepwise methods to ensure the greatest possible separation of the groups.
  • suitable stepwise methods a number of methods are proposed in the literature, for.
  • step-by-step parameters are included in the evaluation, which, based on Wilks Lambda, provide the largest contribution to group separation.
  • Division of anesthesia or intensive EEG can be modeled on Kugler, who, as mentioned in the introduction, designates the awake state with A and the very deep attenuation of the brain function with F.
  • the intermediate stages B to E can be subdivided further, as shown in Table 1 in WO 97/15013 A2 shows. It is also possible, instead of the class designations A to F z. B. to use a scale with numbers, z. B. 100 to 0.
  • a further improvement of the staging is achieved when the age-specific classification functions selected for a subject from stored different age-dependent classification functions. It has been found that a person's EEG has age-dependent characteristics. Put simply, z. As the spectrum in adults with increasing age in the waking state to lower frequencies, while the anesthesia is z. B. reduces the delta power. By taking account of age-specific classification functions, the correct staging can be reliably taken.
  • an indication of the age of the patient whose EEG curves are taken is entered into the evaluation device.
  • the automatic selection of the scheme of staging is carried out by the calculator, taking into account the entered age.
  • the automatic selection of the scheme of staging is performed by the computer taking into account the recorded EEG curves and / or data derived therefrom.
  • This has the advantage that a reliable selection of a suitable scheme of staging can be done automatically, namely on the basis of the already recorded curves, so that the use of the evaluation further simplifies.
  • the consideration of the recorded EEG curves can, for.
  • EEG curves may be examined for certain characteristic curve patterns or for certain statistical data that can be determined from the curves and that are characteristic of particular stages of development of EEGs.
  • the selection of the scheme of staging z. B. on the basis of amplitude data, frequency data and / or means or temporal progressions of amplitudes and / or frequencies of the EEG curves are performed.
  • Absence state is selected as when detecting a minimum proportion of high-frequency signal components, which may be combined with a certain minimum proportion of low-frequency signal components.
  • Absence state is selected as when detecting a minimum proportion of high-frequency signal components, which may be combined with a certain minimum proportion of low-frequency signal components.
  • delta waves can occur as low-frequency signal components. If these low-frequency waves are superimposed with a minimum proportion of higher-frequency waves - as an expression of the effect of anesthetics - the decision can be made that this is a differentiated EEG. Accordingly, a scheme with a greater number of distinguishable stages of the Absence state can then be selected. If the EEG only consists of low-frequency waves, then a scheme with a smaller number of distinguishable stages should be selected.
  • a scheme with a larger number of distinguishable stages of the Absence state can be selected upon recognition of a certain proportion of high-frequency signal components in the EEG curves.
  • a scheme with a larger number of distinguishable stages of the Absence state can be selected.
  • the scheme may be selected with a greater number of distinguishable stages of the Absence state, as it could be an almost completely or completely suppressed EEG ("zero point" stage F).
  • the computer initially starts from a first scheme with a certain number of distinguishable stages of the Absens state and selects at a judgment time for which a sufficient number of EEG data is present upon recognition of certain characteristics in the EEG -Curves a second scheme of staging that has a lesser or greater number of distinguishable staging states than the first scheme.
  • the second scheme of staging in recognizing a minimum proportion of low-frequency signal components in the EEG curves, the second scheme of staging be selected. If the selection condition, ie the recognition of certain characteristics, does not occur, then the computer can eg. B. continue to apply the first schema or select the first schema as the scheme to use for automatically performing the staging.
  • the first scheme z. B.
  • the scheme of a staging for adults are used, for. B. with the staging A to F or the finer division with the stages A 0 , A 1 , A 2 , B 0 , B 1 , B 2 , C 0 , C 1 , C 2 , D 0 , D 1 , D 2 , E and F, as in Table 1 of WO 97/15013 A2 describe.
  • the second scheme of the staging can z. B. have a classification into the stages A, E and F. The aforementioned assignment of the staging to the first and the second scheme can also be reversed.
  • the entered age specification of the patient can also be used.
  • a staging with a reduced number of stadiums can be used. This should be the case for all children in the first two to three months of life, as current own EEG analyzes show.
  • the evaluation unit does not start from the standard divisions A, B, ..., F or 100 to 0, but from a scheme with reduced staging.
  • the evaluator may initially start from a scheme with a greater number of distinguishable stages and then, if necessary, move to a scheme with a lesser number of distinguishable stages during the measurement.
  • At least frequency components in the delta band are evaluated as low-frequency signal components of the EEG curves.
  • frequency components with frequencies below the delta band can also be rated as low-frequency signal components.
  • frequency components with frequencies above the delta band can be regarded as high-frequency signal components.
  • the EEG curves can be evaluated for so-called suppression distances in burst suppression patterns or in suppression EEG.
  • a burst is understood as a sequence of signal waves in an EEG curve. Suppression stretches in EEG curves are curve sections where no bursts appear and the signal has a flat shape compared to the signal waves of a burst. The sections between adjacent bursts are referred to as suppression routes.
  • burst suppression patterns in the evaluation of the EEG curves, detection of burst suppression patterns can be carried out and, when predetermined characteristics of burst suppression patterns occur in the EEG curves, a scheme of staging can be selected which reduces one Number of distinguishable stages in the Absence state, compared to an otherwise selected scheme of staging.
  • characteristics of burst suppression patterns for.
  • BSR burst suppression ratio
  • the burst-suppression ratio indicates what percentage of an EEG curve section consists of suppression stretches.
  • the inter-burst interval (IBI) which is a measure of the distance between bursts, can be used as a characteristic.
  • a further scheme or, depending on the patient's stage of development, several further schedules of the staging can be selected by the computer in addition to a standard scheme of staging, in particular staging with a lower number of distinguishable staging stages than the standard scheme. This allows a particularly well adapted to the state of development of the EEG staging.
  • an indication of the level of development of the EEG is determined based on the selected scheme of staging.
  • This information about the state of development of the EEG can be used internally in the evaluation device in order to influence certain further evaluations or classification functions of the analysis of the EEG signals.
  • the information on the state of development of the EEG can also be output, for. B. via an interface of the evaluation device to another device or visually displayed, z. B. on a display of the evaluation device.
  • the EEG curves is analyzed on curve patterns generated by further biosignals and, if at least one such curve pattern is detected, it is checked whether another scheme is to be selected for the staging than in the case of non-recognition of such curve patterns. In this way, for.
  • artifacts especially movement artifacts and epilepsy typical potentials are detected and both for the selection of the scheme of staging and for the actual stadia classification, ie the classification function, are taken into account.
  • movement artifacts in the recorded EEG curves are determined by means of artifact sensors which may be connected to the evaluation device and the EEG curves are corrected and / or the staging is corrected and / or suppressed and / or introduced on the basis of the determined movement artifacts different scheme of staging selected.
  • artifact sensors can z. B. be designed as deformation sensors of EEG electrodes.
  • deformation sensors may include capacitances which are variable by deformation and whose capacitance change correlates with the deformation of the EEG electrodes.
  • the EEG curves can either be used directly or derived data. So z. B. frequency components are determined by a Fourier analysis or similar analysis. Amplitude values can be evaluated statistically. An amplitude-integrated EEG can also be determined. Amplitude-integrated EEG is a time-compressed representation of amplitudes of an EEG segment. When calculating the amplitude-integrated EEG, the EEG signal z. B. strongly filtered, rectified and smoothed.
  • the object mentioned at the outset is furthermore achieved according to claim 11 by a computer program with program code means set up to carry out a method of the previously described type when the computer program is executed on a computer.
  • the computer program can be executed, in particular, on a computer of the evaluation device explained above.
  • the computer program can be stored on a machine-readable carrier, for. B. on a CD or DVD, a memory stick, on an Internet server or on a storage medium of the evaluation.
  • an evaluation device for evaluating a Absens EEG wherein the evaluation device has at least one computer, EEG signal acquisition means and output means, wherein the evaluation device is adapted to carry out a method of the type described above.
  • the evaluation z. B. be configured to perform the method by the computer executes a computer program of the type described above.
  • the output means may, for. As an interface of the evaluation or a means of visual representation, for. B. be a display.
  • the FIG. 1 shows an evaluation unit 1 for the evaluation of a Absens EEG.
  • the evaluation device 1 has a in the evaluation z. B. arranged on a central board computer 2, the z. B. may be formed as a microprocessor or microcontroller.
  • the evaluation device 1 further comprises a display means 3, z. B. a display. Curves can be displayed graphically on the display means 3 or other recorded and determined data can be reproduced.
  • the evaluation device 1 further has an electrical connection 4, which serves to connect EEG electrodes 7, z. B. via connectors.
  • FIG. 1 It is shown how several EEG electrodes 7 are arranged on the head of a patient.
  • the EEG electrodes 7 are connected to the electrical connection 4 of the evaluation device 1 via cables 6, which are combined in the vicinity of the evaluation device to form a common cable strand 5.
  • the common wiring harness 5 can z. B. by evaluating the existing between the cables capacity values as one or more artifact sensors for the detection of motion artifacts.
  • 1 detection means for detecting capacitances between the lines 6 of the cable harness 5 are provided in the evaluation unit.
  • the FIG. 2 shows exemplary typical output on the display means 3 of the evaluation device 1 data.
  • an upper window z. B one or more EEG curves 10, as they are recorded by the EEG sensors 7, are displayed as a curve over time.
  • a display area 11 the current state of the Absens state, as it is determined by the computer 2 by evaluating the EEG curves, can be displayed.
  • the depth of anesthesia or sedation can additionally be output as a dimensionless number in the range from 0 to 100.
  • the time profile of the determined stages, as shown in area 11, can be indicated as curve 14.
  • the distinguishable stages A to F of the selected scheme of staging are shown.
  • the indication A to F indicates the staging of an adult patient.
  • EEG e.g. B.
  • a less developed EEG e.g. B. in a very young child
  • stage index Dominant EEG characteristics A 100 - 95 Alpha waves B 0 94 - 90 Beta waves, theta waves B 1 89-85 B 2 84 - 80 C 0 79 - 75 Increasing amount of theta waves C 1 74 - 70 C 2 69 - 65 D 0 64 - 57 Increasing amount of delta waves D 1 56 - 47 D 2 46 - 37 E 0 36 - 27 Continuous high delta waves E 1 26 - 20 E 2 19 - 13 Transition to the burst suppression pattern F 0 12 - 5 Burst suppression pattern F 1 4 - 0 Continuous EEG suppression
  • the index values 100-0 could either be adapted to the reduced staging (the entire index range 100-0 would be used), or only a portion of the index range 100-0 could be used, e.g. For example, only the area 100-95 and the area 36-0 would be used.
  • FIG. 3 shows the sequence in the automatic evaluation of the EEG curves by the computer 2.
  • the in FIG. 3 displayed blocks 20, 21, 22, 23, 24, 25 indicate certain evaluation functions or algorithms that are executed on the computer 2.
  • the blocks 20, 21, 22, 23, 24, 25 z. B. as program sections, program modules or subroutines of a computer program that performs the computer 2 may be formed.
  • Block 20 reads in the EEG curves.
  • a selection of a scheme to be used staging of several selectable schemes 26 are shown by the three blocks 26, of which the computer 2 in block 22 of a selects.
  • the computer in block 23 can select one of three selectable classification functions 27.
  • the EEG curves or data determined therefrom are evaluated on the basis of the selected classification function such that a staging of the Absence EEG is carried out using the staging scheme selected in block 22.
  • the determined current state of the Absens state is output.
  • the selection of the staging scheme to be used from the available schedules 26 can be based on the pre-entered age of the patient whose EEG curves are excluded based on the recorded EEG curves and / or data derived therefrom and / or previously selected schemes of staging, as previously explained in detail.
  • this EEG curves can be used after initiating the Absens state.
  • a staging can be made which is adapted to the development of the EEG or the development of the patient and his age.
  • a suitably adapted selection of a classification function from the available classification functions 27 can take place in block 23.
  • the criteria in the selection in block 23 may be one or more of the aforementioned criteria.
  • staging of Absence EEG is twofold adapted to the age and stage of development of the patient.
  • the staging is optimized with respect to the further evaluation of the EEG curves.
  • the data obtained from this are not, as in the case of known evaluation devices, always classified with one and the same schema of the staging, but variably on the basis of that selected in block 22 and classified according to the age or stage of development of the patient optimized staging schemes. So z.
  • an "adult algorithm” is selected for the classification function.
  • the results thus obtained are then classified into one of six stages A through F on the basis of the selected scheme of staging.
  • a "child algorithm” would be used as the classification function.
  • For staging a less fine scheme with z. B. only three distinguishable stages are used.

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Claims (12)

  1. Procédé d'évaluation automatique d'un EEG d'absence, avec lequel des courbes d'EEG sont enregistrées par un appareil d'interprétation (1) et interprétées au moyen d'un ordinateur (2) de l'appareil d'interprétation (1), au moins le stade actuel de l'état d'absence d'un patient étant déterminé lors de l'interprétation à partir des courbes d'EEG au moyen de l'ordinateur (2) à l'aide d'une division en stades de l'EEG d'absence, des stades de la profondeur de l'état d'absence étant différenciés dans la division en stades, et le stade actuel étant délivré, caractérisé en ce qu'un schéma donné de la division en stades est sélectionné par l'ordinateur (2) parmi plusieurs schémas (26) pouvant être sélectionnés, lesquels se différencient par le nombre de stades différents de l'état d'absence, et utilisé pour la réalisation automatique de la division en stades en vue de déterminer le stade actuel.
  2. Procédé selon la revendication 1, caractérisé en ce qu'une indication relative à l'âge du patient, dont les courbes d'EEG sont enregistrées, est saisie dans l'appareil d'interprétation (1) et la sélection automatique du schéma de la division en stades est effectuée par l'ordinateur (2) en tenant compte de l'indication d'âge saisie.
  3. Procédé selon l'une des revendications précédentes, caractérisé en ce que la sélection automatique du schéma de la division en stades est effectuée par l'ordinateur (2) en tenant compte des courbes d'EEG enregistrées et/ou des données qui en sont dérivées.
  4. Procédé selon l'une des revendications précédentes, caractérisé en ce qu'en cas de reconnaissance d'une part minimale définie de composantes de signal à basse fréquence et de non-reconnaissance d'une part minimale définie de composantes de signal à haute fréquence dans les courbes d'EEG, le schéma qui est alors sélectionné présente un plus petit nombre de stades de l'état d'absence différents que dans le cas de la reconnaissance d'une part minimale définie de composantes de signal à haute fréquence qui peuvent être combinées avec une part minimale définie de composantes de signal à basse fréquence.
  5. Procédé selon l'une des revendications précédentes, caractérisé en ce que l'ordinateur (2) adopte tout d'abord un premier schéma présentant un nombre défini des différents stades de l'état d'absence et, à un instant d'évaluation auquel il existe un nombre suffisant de données d'EEG, en cas de reconnaissance de certaines caractéristiques dans les courbes d'EEG, sélectionne un deuxième schéma de la division en stades, lequel présente un nombre inférieur ou supérieur de stades différentiables de l'état d'absence que le premier schéma.
  6. Procédé selon l'une des revendications précédentes, caractérisé en ce qu'au moins les composantes de fréquence dans la bande Delta sont valorisées en tant que composantes de signal à basse fréquence des courbes d'EEG.
  7. Procédé selon l'une des revendications précédentes, caractérisé en ce qu'en plus d'un schéma standard de la division en stades, un schéma supplémentaire ou, suivant le niveau de développement du patient, plusieurs schémas supplémentaires de la division en stades peuvent être sélectionnés par l'ordinateur (2), notamment des divisions en stades avec un nombre de stades différentiables de l'état d'absence plus petit que celui du schéma standard.
  8. Procédé selon l'une des revendications précédentes, caractérisé en ce qu'une indication relative à l'état de développement de l'EEG est définie au moyen du schéma sélectionné de la division en stades.
  9. Procédé selon l'une des revendications précédentes, caractérisé en ce que les courbes d'EEG sont analysées pour y déceler des modèles de courbe générés par d'autres biosignaux et, lorsqu'au moins un tel modèle de courbe est reconnu, un contrôle est effectué pour vérifier s'il faut sélectionner un autre schéma pour la division en stades que dans le cas de la non-reconnaissance d'un tel modèle de courbe.
  10. Procédé selon l'une des revendications précédentes, caractérisé en ce que des artefacts de mouvement dans les courbes d'EEG enregistrées sont déterminés au moyen de capteurs d'artefacts (5) et, à l'aide des artefacts de mouvement déterminés, les courbes d'EEG sont corrigées et/ou la division en stades est corrigée ou inhibée et/ou un autre schéma de la division en stades est sélectionné.
  11. Programme informatique comprenant des moyens de code de programme, notamment programme informatique enregistré sur un support lisible par machine, conçu pour mettre en oeuvre un procédé selon l'une des revendications précédentes lorsque le programme informatique est exécuté sur un ordinateur (2).
  12. Appareil d'interprétation (1) destiné à interpréter un EEG d'absence, l'appareil d'interprétation (1) possédant au moins un ordinateur (2), des moyens d'acquisition de signaux d'EEG (7) et des moyens de sortie (3), caractérisé en ce que l'appareil d'interprétation (1) est conçu pour mettre en oeuvre un procédé selon l'une des revendications 1 à 10.
EP15702706.1A 2014-02-13 2015-01-27 Procédé d'évaluation automatique d'un eeg de diagnostic d'absences, programme informatique et appareil d'évaluation correspondant Active EP3019080B1 (fr)

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DE102014101814.1A DE102014101814A1 (de) 2014-02-13 2014-02-13 Verfahren zur automatischen Auswertung eines Absens-EEG, Computerprogramm und Auswertegerät dafür
PCT/EP2015/051593 WO2015121059A1 (fr) 2014-02-13 2015-01-27 Procédé d'évaluation automatique d'un eeg de diagnostic d'absences, programme informatique et appareil d'évaluation correspondant

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PT3019080T (pt) 2017-04-04
CA2939437C (fr) 2020-01-07
MX2016010454A (es) 2017-02-02
CN105636515A (zh) 2016-06-01
DK3019080T3 (en) 2017-04-10
AU2015217903A1 (en) 2016-09-01
HK1219638A1 (zh) 2017-04-13
IL247224A (en) 2017-07-31
MX364213B (es) 2019-04-16
BR112016010053B1 (pt) 2021-01-26
US10039465B2 (en) 2018-08-07
US20160220136A1 (en) 2016-08-04
JP6517241B2 (ja) 2019-05-22
RU2655133C2 (ru) 2018-05-23
RU2016135639A (ru) 2018-03-16
RU2016135639A3 (fr) 2018-03-16
CA2939437A1 (fr) 2015-08-20
DE102014101814A1 (de) 2015-08-13
ES2620734T3 (es) 2017-06-29
CN105636515B (zh) 2018-08-28
WO2015121059A1 (fr) 2015-08-20
EP3019080A1 (fr) 2016-05-18
AU2015217903B2 (en) 2019-01-31
JP2017505699A (ja) 2017-02-23

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